Some first impressions after finishing the book "TensorFlow for Deep Learning - From Linear Regression to Reinforcement Learning" (by Ramsundar and Zadeh).
The book introduces the concept of tensors, primitives and architectures for deep learning, and the basics of regression, various neural networks, hyperparameter optimization, and reinforcement learning. The art work in the figures is beautiful (something that convinced me to buy the book). The tensorflow code examples can be downloaded from the book's website, making it easy to follow along with the discussion the book.
The book falls a bit short on detailed explanation, however. I found that many times when the discussion in the book was about to get interesting, it referred to other work for details instead. Several architectures were merely "explained" with a figure, no accompanying details in the text.
In addition, although I realize how hard it is to avoid errors in a book, the given linear regressio…

As a CS student, a long time ago in a country far away, I was very interested in AI (Artificial Intelligence), and not just for chess playing programs. In fact, if it weren't for my professor convincing me to continue with compilers and high-performance computing, I may have ended up specializing in the field of AI. Perhaps lucky for me, since AI has gone through many rounds of boom-and-bust.
Nowadays, however, machine learning in general, and deep learning in particular really seem to have taken AI in a very promising new direction. Since I feel machine learning will become an important, if not mandatory skill for computer scientists, I decided to buy a few books on TensorFlow and familiarize myself with the new paradigm.
For starters, I bought the three O'Reilly books below (other recommendations are welcome) and plan to do a few follow-up posts on this topic.